Unified operating system for distributed computing

ABSTRACT

In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource. The micro-capability is executed and configured on the computing resource, and an output of the micro-capability is provided to the target environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/565,324 filed Sep. 9, 2019 and entitled “UNIFIED OPERATINGSYSTEM FOR DISTRIBUTED COMPUTING,” which is a continuation of U.S.patent application Ser. No. 15/839,786 filed Dec. 12, 2017 and entitled“UNIFIED OPERATING SYSTEM FOR DISTRIBUTED COMPUTING,” now U.S. Pat. No.10,452,361, which is a continuation of U.S. patent application Ser. No.15/417,122 filed Jan. 26, 2017 and entitled “UNIFIED OPERATING SYSTEMFOR DISTRIBUTED COMPUTING,” now U.S. Pat. No. 9,841,955, which claimsthe benefit of U.S. Provisional Patent Application No. 62/287,201 filedJan. 26, 2016 and entitled “UNIFIED OPERATING SYSTEM FOR DISTRIBUTEDCOMPUTING,” all of which are incorporated herein by reference.

Aspects of the current disclosure are related to U.S. Nonprovisionalpatent application Ser. No. 14/936,020, which was filed on 9 Nov. 2015,which is a continuation of U.S. Nonprovisional patent application Ser.No. 14/022,033, which was filed on 9 Sep. 2013, and which is acontinuation of U.S. Nonprovisional patent application Ser. No.12/698,361, which was filed on 2 Feb. 2010 and issued as U.S. Pat. No.8,533,675 (included as Appendix A hereto) on 10 Sep. 2013, and whichclaims the benefit of U.S. Provisional Application No. 61/149,179, filedon 2 Feb. 2009, each of which is hereby incorporated by reference.

Aspects of the current disclosure also are related to U.S.Nonprovisional patent application Ser. No. 14/791,820, which was filedon 6 Jul. 2015, and which is a continuation of U.S. Nonprovisionalpatent application Ser. No. 13/846,630, which was filed on 18 Mar. 2013and issued as U.S. Pat. No. 9,075,616 (included as Appendix B hereto) on7 Jul. 2015, and which claims the benefit of U.S. ProvisionalApplication No. 61/612,907, which was filed on 19 Mar. 2012, each ofwhich is hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to operating systems, and moreparticularly, to a unified operating system for facilitating distributedcomputing.

BACKGROUND

Organizations may want to be agile so they may seize opportunities,respond to threats, and generally self-improve. The ability to adaptbehavior for a current context is a fundamental optimization andsurvival strategy. Organizations that fail to evolve with theirenvironment are not sustainable over time.

Advances in technology (e.g. The Web, The Cloud, and now the Internet ofThings) have been transforming the environment for modern organizations.A radical reduction in the cost and latency of global collaboration isenabling new business models (e.g. Netflix; Uber; Airbnb; Skype; etc.)and disrupting incumbents. Organizations that cannot adapt to thisincreasingly distributed and dynamic environment risk displacement.

A challenge for many organizations is that their existing information,communication and automation systems, in and of themselves, do notsupport this new mode of operations. While there exist innumerablesoftware languages and technologies that satisfy a wide variety ofrequirements, a significant challenge is how to flexibly and dynamicallyconnect people, information, systems and devices for a new class of‘smart’ processes.

SUMMARY

Various embodiments of the present disclosure include systems, methods,and non-transitory computer readable media configured to detect one ormore real-time events and determine a context based on the one or morereal-time events. An application model is fetched based on the contextand meta-data associated with the one or more real-time events, theapplication model referencing one or more micro-functions, eachmicro-function being a declarative model of one or more atomic functionsand including at least one pre-condition descriptor and at least onepost-condition descriptor. A functional graph is constructed based onthe one or more micro-functions of the application model. The one ormore micro-functions are transformed into a plurality ofmicro-capabilities, each micro-capability of the plurality ofmicro-capabilities being capable of satisfying at least onepre-condition of the at least one pre-condition descriptor and at leastone post-condition of the at least one post-condition descriptor, bydetermining at least one computing resource for execution of at leastone of the plurality of micro-capabilities by matching pre-conditions ofthe at least one micro-capability of the plurality of micro-capabilitiesand matching post-conditions of the at least one micro-capability of theplurality of micro-capabilities; and enabling execution andconfiguration of the at least one micro-capability on the at least onecomputing resource by providing access in a target environment to anapplication program interface (API), the API capable of calling the atleast one micro-capability to configure the at least onemicro-capability on the at least one computing resource and execute themicro-capability on the at least one computing resource. A request isreceived to execute and configure the at least one micro-capability onthe at least one computing resource from the target environment. The atleast one micro-capability is executed and configured on the at leastone computing resource, and an output of the at least onemicro-capability is provided from the at least one computing resource tothe target environment.

In some embodiments, the one or more real-time events include areal-time system request of the target environment. In relatedembodiments, the real-time system request indicates an applicationobject in a header of the real-time system request, and the contextincludes an identifier indicating the application object. In relatedembodiments, the output of the at least one micro-capability from the atleast one computing resource comprises a customized representation ofthe requested application object.

In some embodiments, the application model, the one or moremicro-functions, the plurality of micro-capabilities, and the at leastone computing resource are modeled from a root object defining baseproperties and capabilities.

In some embodiments, the systems, methods, and non-transitory computerreadable media further comprise a plurality of computing resources,wherein the plurality of computing resources includes the at least onecomputing resource, the plurality of computing resources providing avirtualized pool of resources, each of the plurality of computingresources being a virtualized infrastructure, a physical infrastructure,or a software-controlled device.

In some embodiments, the plurality of computing resources aredistributed across a multi-dimensional network of nodes connected by oneor more links.

In some embodiments, the at least one computing resource includes one ormore computing resource descriptors, the one or more computing resourcedescriptors including an API descriptor associated with the API. Inrelated embodiments, the one or more computing resource descriptorscomprise any of one or more local interfaces, local capabilities, localservices, supporting services, and operating system kernels. In relatedembodiments, the one or more computing resource descriptors facilitatemanagement of thread handling, resources, and memory spaces for the atleast one computing resource much as an operating system performs for asingular virtual infrastructure unit, physical infrastructure unit, orsoftware-controlled device.

Various embodiments of the present disclosure include systems, methods,and non-transitory computer readable media configured to provide asolution for the dynamic design, construction, deployment, use, andmodification of software applications on a plurality of computingresources that operate collaboratively and in parallel.

In various embodiments, the systems and methods described herein arebased on a multi-paradigmatic design, bridging Metaprogramming,Functional Programming, Object Oriented Programming and Model-DrivenEngineering concepts, which results in a real-time interaction-drivensystem that dynamically constructs the components it needs in a dataflowpipeline of virtual functions to resolve a specific system request as acontextually optimized service.

In some embodiments, applications are declarative models of requirementsfor desired software behavior(s), which reference, directly orindirectly, Micro-functions. The application “intent” is realized by aSoftware Agent, which processes a cascade of models to render concretebehavior. The Agent resolves references for Micro-functions and executesthem as a dataflow pipeline of contextualized Micro-capabilities ondynamically provisioned and configured Computing Resources on ashort-lived thread

Various embodiments of the present disclosure include systems, methods,and non-transitory computer readable media configured to provide afederated computer system consisting of at least two computing devicesand a logical and physical fabric.

In some embodiments, a system comprises a plurality of computingresources, each of which may be a tangible embodiment (e.g., a computingdevice) or a virtual embodiment of a group of a plurality of computingresources. The traits of these resources may be identified and madeaccessible through a classification scheme, providing unified access andusage of these resources independent of their embodiment.

In some embodiments, systems, methods, and non-transitory computerreadable media are configured to provide a plurality of sets of smallsoftware elements, where each element embodies a single function withoutside effects, utilizing the resources of the underlying systemembodiment through selective usage of identified traits as required forthe function's embodiment. These small software elements may constitutecontextualized micro-capabilities, which may be uniquely identified, anddefined by their preconditions, post-conditions, and/or an embodiedalgorithm.

In some embodiments, systems, methods, and non-transitory computerreadable media are configured to provide a plurality of sets of modelsand metamodels, which describe and define the interfaces and relatedsemantics of all components of a system described herein. A metamodelmay define a declarative language for the creation of a plurality ofmodels describing the traits of the embodiment of the computingresources. These models may describe these traits in their structuralaspects and in their exhibited semantics (e.g., application programinterfaces (APIs)).

In some embodiments, a metamodel also may be used to define a languagefor the creation of a plurality of models, each of which may define thestructural aspects and semantics (e.g., APIs) of a singlemicro-capability. The semantics defined by the model may include thealgorithm embodied by the described micro-capability. Additionally, thesemantics may include the preconditions that must be satisfied beforeinvoking any execution of the algorithm embodied by themicro-capability. Preconditions may include the structure and type ofany information required as input for the algorithm embodied by themicro-capability, any specific conditions of the surrounding environmentrequired for the execution, and/or the like. These environmentconditions may include all required supporting software and traits ofthe computing resource hosting the micro-capability embodiment. Themodel may further define the post-conditions holding after completion ofthe execution. Post-conditions may describe the structure and type ofthe execution results, their meaning, the specific conditions of thesurrounding environment that must be met for a transfer of executionresults and control flow, and/or the like.

In some embodiments, systems, methods, and non-transitory computerreadable media are configured to provide ways and means to organizemicro-capabilities. Micro-capabilities may follow two principlesdemanded by the paradigms of Model-Driven Architecture® and FunctionalPrograming. These principles are separation of concerns and an absenceof side effects. With respect to micro-capabilities, this means thateach micro-capability may implement a single, distinct and atomicalgorithm. It may perform the execution of this algorithm exclusively onthe basis of the prevailing preconditions at the moment of invocationand may deliver results only in the form specified by thepost-conditions. Besides these results, the execution may have nofurther effects on the surrounding environment. The conditions may bemodeled as a micro-function, an implementation-independent definition(e.g., an abstract definition), sufficient to describe and classify aplurality of equivalent micro-capability embodiments hosted on aplurality of computing resources. The cascade of models used by thesystem transforms the micro-function into a contextualized embodimentfor each use. These embodiments of micro-capabilities may be organizedin a plurality of collections, like repositories or libraries. However,the definition and classification of micro-capabilities may be singularand common for all their embodiments. This enables the classification ofall micro-capabilities independent of their embodiment, and provides anability to maintain the plurality of classifications definitions for allmicro-capabilities in a common catalog structure. This catalog mayafford an ability to automatically derive executable solutions from theplurality of micro-capabilities.

In some embodiments, systems, methods, and non-transitory computerreadable media are configured to provide a methodology and embodiment toconstruct executable software applications from micro-capabilities.Applications may be modeled using a declarative modeling methodology andenvironment. Applications may be defined in structure and function by adeclarative model. This model comprises a plurality of sets of modelsthat describe functional elements of the application as referencedmicro-functions. A platform-independent embodiment (e.g., an abstractembodiment) of the application may then be constructed by assembling asolution graph from micro-function definitions based on algorithms andpre- and post-conditions. A software agent may perform a recursivetransformation and combination of contextualized micro-capabilities. Theresult may be a complete core application, however in a representative(e.g., abstract) form, which means there is yet no real and executableembodiment of the application, but a complete composition plan listingall required micro-capabilities and their combination. After decidingthe deployment target, the agent may retrieve the micro-capabilityembodiments built for and compatible with the traits of the executionenvironment and construct the application embodiment based on thecomposition plan (e.g., an abstract composition plan). The compositionplan may provide a real-time system by deploying and executing thesoftware as a dataflow pipeline of contextualized Micro-capabilities ondynamically provisioned and configured Computing Resources in ashort-lived thread. Or, since the composition plan is completelyplatform-independent, it may be pre-calculated and stored, and thenreused for a plurality of deployments on a plurality of targetplatforms.

In some embodiments, systems, methods, and non-transitory computerreadable media are configured to extend the technology presented hereininto a federation over a plurality of computing resource embodimentsdistributed across a multi-dimensional arrangement of nodes. These nodesmay represent physically separated embodiments of one or more computingresources, or may represent isolated logical computing resources withina larger hosting environment. An example for the latter would be atypical datacenter arrangement, where a plurality of virtual servers ishosted by a single physical server system. Each embodiment of acomputing resource may represent an autonomous, self-contained system,connected to the plurality of surrounding computing resource embodimentsby the means of a communication facility. These embodiments may expectthis communication facility to provide a standardized set ofpeer-to-peer communication services for synchronous, asynchronous andad-hoc messaging. The underlying technology to realize these servicesmay vary from case to case. The plurality of interconnected computingresources allows applications to include models for DistributedOperating System concerns including, but not limited to, threadhandling, resource management, memory spaces, and/or the like.

In some embodiments, a fabric of interconnected computing resources maybe described as nodes, interconnected by links. Each node may host acomplete embodiment of an execution environment capable of executingapplications constructed from an arrangement of micro-capabilities.However, not all nodes need to have the ability to perform theconstruction process, or require this ability. This distinction allowsthe inclusion of nodes with very limited resources into the federatedarrangement without losing the benefits of the model-driven automatedcomposition of applications. The minimum abilities from such a limited,or basic node may include: (1) the presence of an execution environmentthat supports the execution of an application composed frommicro-capabilities without relying on resources outside of that node;(2) the ability to report the traits and state of this node to othernodes; (3) the ability to receive and/or replace complete applicationsand/or individual micro-capabilities, communicated to that node by othernodes that are part of the fabric; (4) the ability to initialize, start,stop, reset, delete, or otherwise influence loaded applications based oncommunications received from other nodes in the fabric; and/or the like.In an embodiment, there must be at least one node in the fabric of nodesthat has extended abilities beyond the abilities of a basic node,however, there is no upper limit on the plurality of such extendednodes.

In some embodiments, systems, methods, and non-transitory computerreadable media are configured to provide a method of a rigorousstructural and semantic description of APIs for all micro-functions andmicro-capabilities. Based on this, an automated selection, matching andcombination of micro-capabilities may be provided. This may beaccomplished on the model level and therefor independent of theembodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an architecture of a computing resource according to anembodiment.

FIGS. 2A, 2B and 2C show illustrative variations of an extendedarchitecture pattern according to embodiments.

FIG. 3 shows illustrative interconnected nodes according to anembodiment.

FIG. 4 shows an illustrative micro-capability meta-architectureaccording to an embodiment.

FIGS. 5A, 5B and 5C show illustrative aspects of micro-capabilitiesaccording to embodiments.

FIG. 6 shows an illustrative construction of an application modelaccording to an embodiment.

FIG. 7 shows an illustrative distributed environment according to anembodiment.

FIG. 8 shows an illustrative process for selecting and chainingmicro-capabilities according to an embodiment.

FIG. 9 shows an illustrative process of deployment and configuration ofmicro-capability-based applications according to an embodiment.

FIG. 10 shows an illustrative environment for developing a functionalmodel-based application according to an embodiment.

DETAILED DESCRIPTION

Applications are already rapidly evolving from centrally defined andcontrolled ‘monoliths’ to composite applications of Services and APIsthat need to cross silos, organizational boundaries, domains, protocolsand technologies. However, the prevailing application developmentpractice is to develop software solutions on top of discrete softwarecomponents required to process functional requirements (i.e.“middleware”), which results in siloed, static and brittle solutions.Middleware software components (e.g. Enterprise Service Buses; BusinessProcess Management Suites; Complex Event Processing software; BusinessRules Management Systems; API Gateways; Integration Technologies;Business Intelligence tools; etc.); are pre-integrated to form amiddleware “stack”. Each component generally represents a long-runningsynchronous process, waiting for inputs to perform transformations andoutput results (i.e. operations). Since all connections betweenapplications and components, components and components, and componentsand their underlying infrastructure (i.e. compute, storage and networkresources) are established a priori creating a tangle of fixeddependencies (i.e. “tight-coupling”), any change to this solutionarchitecture may cause applications to fail to perform desired targetbehavior. In addition, this design is based on a linear series ofmessage-passing for communicating application state between componentprocesses with the output of one component being the input to the nextcomponent, for an ordered series of operations (i.e. “orchestration”),which ultimately results in the targeted application behavior. Themiddleware stack introduces its own collective overhead, which impactsdevelopment considerations regarding compute and input-output intensiveapplications. The linear message-passing does not lend itself to a broadreal-time evaluation of application state making it more ‘expensive’from a processing perspective to dynamically customize applicationbehavior per interaction (e.g. personalize user-experience; optimizetransaction; etc.). Also, since application state related to executingfunctions is distributed across components, if any component process(i.e. executing thread) fails, state can be lost, leading to a failedtransaction with no direct compensations. As a result of these inherentlimitations, the prevailing application development practice involvingmiddleware components is a rate-limiter on emerging businessrequirements, business agility writ-large and the transformation ofmodern companies into digital businesses.

In middleware software development, adaptations are traditionallyaccomplished through a directed and comparably slow process, constrainedby the nature of the organizations and their systems. To date, exceptionand change management still generally remain inefficient, disconnectedand largely manual processes.

Traditional engineering approaches are not necessarily capable ofsupporting rapid and dynamic adaptation of software systems in thedimensions of structure, functionality and spatial federation. Thecommon practice of specifying and creating technical systems as a wholeconsists in the majority of cases of a single process stream, whichtypically leads to products forming large, cohesive units, with theircharacteristics “cast in stone” at a very early stage. This approach maybe suitable for long-lasting infrastructure projects, like bridges orbuildings, but not for information technology. However, to date, asimilar single-stream process is a quite common practice in softwareengineering too, where it becomes more and more inappropriate, as itresults in monolithic products that are difficult to maintain or change,and impossible to prove for correctness.

Many approaches have been proposed over the past decade to change thisand make the development process in software engineering, or informationtechnology in general, more agile and adaptable. A promising methodologyamong those is the Model-Driven Architecture® (MDA®) proposed by theObject Management Group® (OMG®). This methodology prescribes a cascadeof modeling activities as the principle design phase for a system. Thecascade starts with a high-level and conceptual model (e.g., abstractconceptual model), derived as a response to, and fulfillment of thesystem requirements. In the MDA® methodology, a sequence of modeltransformations follows that transition step-by-step from the conceptualmodel towards one or more implementable platform-specific models (PSM).During this cascade of model transformations and extensions, more andmore external information is introduced to convert the concept intoimplementable specifications. While the pure and literal application ofthe MDA® methodology provides ample opportunity for change andverification along that cascade of model transformations andrefinements, it still generally results in a monolithic end product.Though derived through a model-based and flexible design phase, a PSMimplementation is itself a static output (i.e. the PSM is compiled to anon-portable, non-dynamic, non-adaptive, non-evolutionary platform).

Solution Overview

Changes in the way we work may require us to re-think applicationarchitecture and the middle-tier. The conventional middleware stack isbeing disrupted by the broader technology movements mentioned above.Successful organizations are complex adaptive systems. In someembodiments, organizational systems support a complex adaptiveoperation, which is capable of adapting to a changing environmentthrough dynamic networks of interactions and individual and collectivebehaviors that mutate and self-organize in response to changes andevents to increase an agility and sustainability of the organization.

In some embodiments, the middle-tier is a lightweight,horizontally-architected, elastically scalable layer between theend-points an organization has access to and the behavior it wants (i.e.application logic). In this manner, middleware is re-imagined as avirtualized software-defined server with a distributed operating system,which supports the composition, run-time and management of highlyresponsive, connected and adaptive applications with related middlewarefunctionality realized as dynamic services, rather than discretesoftware components.

Re-conceptualization of the middle-tier as a virtual software-definedserver with a distributed operating system may allow organizations tomore naturally express themselves as complex adaptive systems.

Adaptation, as it relates to the new middle-tier may include an abilityto handle each system interaction individually, as its own dynamicallyinterpreted complex-event, in order to construct a custompolicy-controlled response based on real-time operational intelligence.Instead of deploying processes that simply execute fixed models forstandardized responses from static connections, a fully dynamic“situationally-aware” approach may natively support variance at themodel level to personalize user-experiences, contextualizepolicy-enforcement, recommend or direct next-best-actions (e.g., logicalworkflow), and/or the like, while at the metamodel level usinginteraction-metadata to translate policies that drive the discovery andbinding of just the relevant system objects to resolve and record theevent.

An ability to support variance at the model level affords the potentialfor in-flight user-driven policy-constrained modifications for a singleinstance without disruptive exception management processes. Likewise, anability to support variance at the metamodel level affords the potentialfor adaptation of any of the participating system objects, withoutdisruptive change management processes, as the objects, or even specificversions of each object, may be found and/or bound in real-time (e.g.,dynamic composition and automated interoperability for non-disruptiveAPI evolution).

A flexible and adaptable middle-tier may be extended by queries,functions, predictive analytics machine-learning algorithms, and/or thelike, which may be other endpoints that may be referenced at either themodel or metamodel level.

In an embodiment, the MDA® methodology is combined with principles fromFunctional Programming, Functional Reactive Programming, DataflowProgramming, Metaprogramming, and the like, in a multi-paradigmaticsolution. Such a combination makes a new approach explicit: instead ofproducing a monolithic application as specified by the PSM, build afunctional equivalent application as an arrangement of lightweightfunctional units. These functional units may conform to functionalelements found in the conceptual model or one of its early derivativesin the model cascade. Each such functional unit may implement exactlyone function without further side-effects and/or conform to a commoninterface specification. From the perspective of FunctionalDecomposition, this embodiment includes, but is not limited to,functions, business entities, policies, services, applications, and/orthe like, that are all described by metadata in a common pool.Interactions of this embodiment may take the form of functional chainsconfigured by metadata as directed by models. Essentially, this maysatisfy the criteria for the “Pipe-and-Filter” architectural pattern,which is also part of the REST architectural style. From a systemperspective, these functional chains include, but are not limited to,system functions that perform middleware capabilities, system policiesfor non-functional concerns, and/or the like, that are assembled fromthe functional units as part of the model cascade.

Collectively, these high-level principles may be brought together in asolution that constructs the components it needs in a pipeline ofvirtual functions or “micro-capabilities” that exist ephemerally toresolve a specific request as a dynamically realized service. Thisstands in stark contrast to the current middleware stack paradigm ofmanually integrating purpose-built static components, and then writingstatic applications across them that are inherently tightly-coupled andbrittle, inhibiting desired organizational agility.

Building an application as an arrangement of functional units may openup the application for change (by altering the arrangement and/orsubstituting functional units with alternatives), but also may provide abasis for spatial extensions. Functional units belonging to oneapplication may be placed on alternative processing facilities, thusallowing for spatial distributed and potentially parallel execution.

While modeling is an approach to specify the desired product in variouslevel of abstraction, meta-modeling may provide a capability to definethe modeling language used to specify the product, and to specify allaspects of the production environment used to create and deploy theproduct, as a set of models. As a consequence, the productionenvironment itself opens up for easy adaptation, which in turn providesyet another dimension of adaptability to the final product. Using acommon approach for modeling and meta-modeling, the boundary between“design-time” and “run-time” may be allowed to vanish. Well-definedalgorithms and methodologies exist to select functional units and tosupport construction of an arrangement representing the application.

By applying Meta-modeling concepts to MDA®, some embodiments are able tointerpret models based on real-time interaction context, using adataflow pipeline to perform transformations on-demand, resulting indeclarative and dynamic solutions, this is contrast to traditionalapplications of MDA® for solution development where models are compiledin advance creating specific source to target transformations a priori,resulting in imperative and static programs.

In some embodiments, all elements and components of the system,including, but not limited to, Applications, Micro-functions,Micro-capabilities, Computing Resources, and the like, are modeled froma Root Object that defines base properties and capabilities (e.g.identifier, metadata descriptor, etc.). The cascade of models used togenerate applications may provide a form of dataflow-based polymorphicinheritance, contextualizing all models from a common base-object,allowing the application of Object Oriented Design Patterns (e.g.Façade, Factory, etc.) to assembled Functional Programs.

Solution Components

Solution components, according to some embodiments, may be defined asfollows:

Applications are declarative models of requirements for desired softwarebehavior(s), which reference, directly or indirectly, Micro-functions.Application “intent” is realized by a Software Agent, which processes acascade of models to render concrete behavior. An embodiment ofApplication is described below.

Micro-functions are declarative models of atomic functions, side-effectfree and independent of implementations that are consumed byApplications. Micro-functions are explicitly modeled. Micro-functionsare transformed into contextualized Micro-capabilities during execution.An embodiment of Micro-functions is described below.

Micro-capabilities are ephemeral processing intermediaries that aredynamically constructed by the Software Agent when implementing aMicro-function as a component of a solution. An embodiment ofMicro-capabilities is described below

Computing Resources are declarative models of infrastructure resources(i.e. virtual and tangible compute, storage and network nodes,centralized and distributed), which collectively present a hybriddistributed network as a unified pool of resources (i.e. a computationalmesh, lattice, fabric, etc.). An embodiment of Computing Resources isdescribed below.

A Software Agent is a proxy for the System, handling requests andinterpreting the Declarative language in order to process the cascade ofmodels to realize intended application behavior. Software Agentsdynamically construct, alter and adapt applications in real-time, byrearranging and reconfiguring micro-capabilities based on changes in anyof the models (or objects they represent). Such behavior would not bepossible using traditional model-driven approached.

In some embodiments, a Software Agent is an application realized by arecursive process:

-   -   a. Software Agent identifies “intent” by fetching an application        model    -   b. Constructs a corresponding functional graph based on        referenced Micro-functions    -   c. Transforms Micro-functions into contextualized        Micro-capabilities, capable of satisfying the pre-conditions,        post-conditions and functional characteristics of each        Micro-function by:        -   i. Determines appropriate Computing Resource for execution            of the Micro-capability by matching pre-conditions,            post-conditions and functional characteristics between their            respective descriptors        -   ii. Uses generated API descriptors for the Computing            Resource to execute, deploy, connect or configure it as            required by the Micro-capability        -   iii. Executes the configured Micro-capability on the            Computing Resource    -   d. Updates the process, chaining Micro-capabilities:        -   i. Using generated API descriptors to connect            Micro-capabilities    -   e. Recursively evaluates and chains Micro-capabilities,        repeating chain until the “intent” is met.

Other Embodiments of Software Agents are described below.

Computing Resource

In some embodiments, all applications, end-user and system applications,may utilize a logically uniform computing resource to host theirexecution. FIG. 1 shows an architecture of a computing resourceaccording to an embodiment. Independent from the actual embodiment, eachComputing_Resource 110 may exhibit a uniform interface and a well-knownset of services, divided into two categories: Local interfaces andservices, represented by the Traits_Abstraction 130, andfederation-related communication and remote access or invocationservices provided by the Federation_Fabric_Endpoint 120. This pluralityof interfaces and services may form a fixed and stable contractComputing_Resources provide, regardless of how they are embodied.

Traits are a relatively new concept, originally introduced inconjunction with the C++ meta-programming concept, but are now anintrinsic part of the latest generation of programing languages, likeScala, Swift, and others. Conceptually, traits are very similar tointerfaces in that they provide a contract consisting of declarationsfor data structures and operations, but in contrast to regularinterfaces, traits may also directly embody implementations for datastructures and operations.

Computing_Resources require the incorporation of traditional operatingsystem functionality to fulfill their service contract. The followingdiscussion extends the architecture pattern introduced in FIG. 1 byadding an operating system component and a platform support component.Three illustrative common variations of that extended architecturepattern are discussed in conjunction with FIGS. 2A, 2B and 2C. Howeverthe plurality of variations is not limited to the shown three variants.Other variants may include, but are not limited to, nodes providingstorage capabilities, networking capabilities and/or the like. To thisextent, any variant fitting the pattern is implicitly included.

FIG. 2A shows core operating system support required by theComputing_Resource 110 may be provided through inclusion of aDedicated_MicroKernel 221 into the Traits_Abstraction 130. Microkernelsmay provide only very basic operating system services, only thoserequiring a privileged and protected execution environment. All otheroperating system services may be expected by the microkernel to beprovided by processes operating in unprivileged space (often referred toas “servers”). The plurality of these services may be aggregated intothe Platform_Support component 210 of the extended Computing_Resourcearchitecture pattern.

In an illustrative more “traditional” operating system environment,shown in FIG. 2B, the OperatingSystem_Kernel 222 may provide a muchricher service set out of its privileged execution environment than amicrokernel. As a consequence, the Platform_Support component 210 maybecome more lightweight.

A third example variation for an extension of the basic architecturepattern, as shown in FIG. 2C, addresses virtualization. A fundamentalconcept of virtualization is to host a plurality of “guest” operatingenvironments on a single “host” platform including the system's hardwareand core operating system services. In this discussion, the “host” partof virtualization is not explicitly discussed and each “guest” systemmay be treated as an independent Computing_Resource.

In FIG. 2C, a Virtualization_Kernel 223 takes the position of theoperating system component. The form and “shape” of this kernel may varywidely dependent on the virtualization technique used.

Common (in architecture terms) to the three illustrative architecturalalternatives presented in FIGS. 2A, 2B and 2C is the Platform_Supportcomponent 210. An embodiment of this component may be different for anyinternal architecture variant of the Traits_Abstraction 130, as depictedin FIGS. 2A to 2C. The Platform_Support component 210 may provide abridge between the provided features and interfaces of the operatingsystem component 221, 222 and 223 and the fixed interface contractpromised by the Traits_Abstraction 130.

Federation Fabric

A feature of an embodiment is the arrangement of Computing_Resources 110as a federated and distributed computing environment. Two nodes areshown in FIG. 3 as a representation of a plurality of interconnectedComputing_Resource nodes with no conceptual upper limit of the number ofnodes forming this plurality. Collectively a set of interconnectedComputing_Resource nodes provides a unified pool of resources (i.e. acomputational mesh, lattice, fabric, etc.). This unified pool allowsapplications to include models for Distributed Operating System concernsincluding, but not limited to, thread handling, resource management,memory spaces, and/or the like.

Two nodes may be connected by means of a physical connectivity 320between Federation_Fabric_Endpoints 120 embedded in the respectiveComputing_Resources 110. “Physical” in this context means any embodimentof a communication means capable of supporting a live peer-to-peerconnection based on a standardized peer-to-peer communication protocol.This protocol requirement may exist only for the exhibited peer-to-peerconnection; the underlying “physical” implementation may be free to useany reasonable means as long as the peer-to-peer requirements may beguaranteed and as long as embodiments of corresponding communicationports 310 compatible with both, the embodiment of the incorporatingFederation_Fabric_Endpoint 120 and the characteristics of the “physical”connection scheme 320, are available.

The peer-to-peer connectivity across the communication path 320 maysupport the virtual direct connectivity between Computing_Resources 110as shown as a dashed line 330 in FIG. 3, as well as a virtual directconnection between Traits_Abstractions 130, as shown by dashed line 340.

MicroCapabilities

FIG. 4 introduces an illustrative micro-capability meta-architectureaccording to an embodiment. Micro-capabilities may be small functionalunits providing a single function without side effects.Micro-capabilities are ephemeral intermediaries for executing thefunction, constructed as part of the cascade of models forming aModel-driven pipeline. Applications and services may be subsequentlycomposed by forming arrangements of micro-capabilities. This process ismodel-based and model-driven. FIG. 4 outlines an illustrativethree-level meta-architecture for micro-capabilities.

The modeling language for all micro-capabilities may be defined by themicro-capability meta-component named MicroCapability 400, which is partof a metamodel according to an embodiment. The meta-componentMicroCapability may be a logical blueprint for the modeling of aplurality of function micro-capabilities, of which three are shown asexamples for the whole plurality 411, 412, 413. The fact that themeta-component MicroCapability may be a defining metamodel for thoseFunction micro-capabilities is expressed by the dependency relationships430, stereotyped as «definedBy».

Each of the functional micro-capability (component models) may beembodied by at least one, or a plurality, of micro-capabilityimplementations, embodying the algorithm, preconditions, andpost-conditions specified in the corresponding micro-capability model.This is shown example by items 421, 422, 423, 424 and 425. Aninstantiation relationship is shown as dependency 440 stereotyped«instantiates». Each modeled micro-capability, as for example Function1411, may be instantiated by a plurality of sets of micro-capabilityinstances. These sets may span a two dimensional space, where variationsin the embodiment of the concrete algorithm implementing the abstractalgorithm defined by the modeled micro-capability component (Function1in this example) may represent one dimension, and the dependency of themicro-capability embodiment on the plurality of possible embodiments ofthe target Computing_Resource 110 may represent another dimension.

FIG. 5A shows an example expanded view of the metamodel provided by ameta-component MicroCapability 400 according to an embodiment. It showsan illustrative definition of the provided interface 501, which mayexpose the means and conditions to invoke the function provided by theMicroCapability; and an illustrative definition of the requiredinterface 502 that may provide the processing results. Any receivingentity has to match this required interface in order to receive thefunction results correctly.

The MicroCapability Descriptor 510 may define an information modelholding classification keys and formal descriptors about the functionalcharacteristics and properties of the MicroCapability. This informationmay be complemented by an API_Descriptor 511, which may define theinformation model describing the provided and required interfaces, andall associated pre-and post-conditions. Dependency 503 may refer to theprovided API 501 (and related post-conditions), while Dependency 504 mayrefer to the required API 502 and related post-conditions. When amicro-capability is modeled based on this metamodel, the MicroCapabilityDescriptor and API_Descriptor may be instantiated. Their embodiment mayhold all necessary information to create, combine and deploy anembodiment of that modeled micro-capability.

FIG. 5B shows an example arrangement of three modeledmicro-capabilities, Function1 411, Function2 412 and Function3 413according to an embodiment. These micro-capabilities may be fullydescribed by their corresponding embodiments of MicroCapabilityDescriptors with names corresponding to the type name of the modeledmicro-capability they describe. These are shown as 531 for Function1,532 for Function2, and 533 for Function3. Connecting interfaces may bedescribed by two descriptors, one for the provided interface of thedownstream micro-capability and one for the required interface of theupstream micro-capability. These two interfaces may match to allow aconnection. The pairing of provided and required interfaces, shown as521, may provide the connectivity between micro-capability Function1 411and micro-capability Function2 412. Correspondingly, embodiments ofAPI_Descriptors may describe the details of this interface pairing 521.Part of API_Descriptor 541 may describe the required interface ofmicro-capability Function1 411, and part of API_Descriptor 542 maydescribe the matching provided interface of micro-capability Function2412.

An equivalent arrangement may exist for the interface pairing 522between micro-capability Function2 412 and micro-capability Function3413, using part of API_Descriptor 542 to describe the required interfaceof micro-capability Function2 412, and part of API_Descriptor 543 todescribe the matching provided interface of micro-capability Function3413.

FIG. 5C shows illustrative embodiments of MicroCapability Descriptorsand owned API_Descriptors, which may be stored as part of theMicroCapability metadata repository. The plurality of descriptors maycomprise elements of an ontology and form a searchable catalog ofplatform-independent micro-capability metadata, which may be used by theComposer Agent 720 in FIG. 7 (e.g., an agent configured to perform acomposer role) to construct arrangements of interface-compatiblemicro-capabilities, which in their totality may implement the desiredapplication functionality.

Application Construction

An example construction of an Application model 600 is shown in FIG. 6by incorporating and combining the models of three micro-capabilities,Function1 411, Function2 412 and Function3 413. Two Applicationembodiments, app1 641 and app2 642 may be constructed and deployed intoan embodiment of a Computing_Resource named node1 610. The constructionand deployment act is symbolized by the pair of dependency relationshipsstereotyped «instantiates» 650. These example embodiments of themicro-capabilities to be incorporated into the Application embodiments,following the Application model 600, may be received from theMicroCapability implementation repository 620, which can hold a set ofanonymous embodiments of micro-capabilities, as shown by 631, 632, 633and 634. Copies of these may be incorporated into the Applicationembodiments and named accordingly.

This example is shown by 421, 423 and 425 for app1 641, and by 422, 423and 424 for app2 642. The fact that these Application embodiments 641,642 may be composed from micro-capability embodiments stored in theMicroCapability implementation repository 620 is expressed by thedependency relationships stereotyped «composedFrom» 660.

The MicroCapability implementation repository 620 may only logically bepart of the Computing_Resource embodiment node1 610. In this example,the embodiment of repository 620 may be collocated with anotherComputing_Resource embodiment and connected with the currentComputing_Resource embodiment node1 610 by means of the peer-to-peercommunication services provided by the Federation_Fabric_Endpoints 120.

Complete System Architecture

FIG. 7 shows an example distributed environment in some embodiments.FIG. 7 depicts an example chain of three Computing_Resources 110connected by materialized communication links 320, which are linking theFederation_Fabric_Endpoints 120 through the communication ports 310built into the Federation_Fabric_Endpoints. Virtual peer-to-peercommunication between the Computing_Resources is shown as a dashed line330. Likewise, virtual peer-to-peer communication between TraitsAbstractions is shown as dashed line 340. These virtual peer-to-peerconnections can be realized by the Federation_Fabric_Endpoints overtheir communication link 320.

The shown three Computing_Resources may differ only in the servicesassociated with them. They may also differ in the method of theirembodiment, but on a logical level, they may be equal, as guaranteed bya provided common service contract. The Computing_Resource shown in thecenter of FIG. 7 may host a complete system stack, comprising theMicroCapability implementation repository 620 and the related DeployerAgent 710 (e.g., an agent configured to perform a deployer role); theMicroCapability metadata repository 550 and the related Composer Agent720 (e.g., an agent configured to perform a composer role); and theapplication development environment Design_Studio 730. The Design_Studiomay incorporate an embodiment of the Metamodel repository 740 that cancontain all definitions of any model element used within the system. TheDesign_Studio 730 may have associated private application model storage733.

In some embodiments, the Computing_Resource shown to the left in FIG. 7may represent a minimum configuration of a node within the federatedcomputing environment described herein. In addition to the coreComputing_Resource 110, it may embody the Deployer Agent 710, which mayhave control over the Computing_Resource through the connection 712, butreceive embodiments of micro-capability functions through the virtualconnection 713 to a closely located MicroCapability implementationrepository 620. In various embodiments, this connection 713 may berealized through a communication connection hosted by theFederation_Fabric_Endpoints 120 and their materialized communicationlink 320. In some embodiments, the peer-to-peer protocol used by theDeployer Agent 710 to the MicroCapability implementation repository 620may follow representational state transfer (REST) principles.

The Computing_Resource shown on the right side in FIG. 7 also may host aDeployer Agent 710, as well as its private embodiment of theMicroCapability implementation repository 620. This configurationdemonstrates the federation abilities of the repository architecturedescribed herein. While repositories, the Metamodel repository 740, theMicroCapability metadata repository 550 and the MicroCapabilityimplementation repository 620 in their totality may represent onelogical repository structured with multiple meta-levels, the embodimentmay use federation and replication techniques to separate implementationconcerns and to improve performance of the overall system by collocatinginformation and processing units.

Composition, configuration, and deployment of applications may beaccomplished by autonomous agents on two logical levels. On theplatform-independent model level, the Composer Agent 720 may acceptapplication models from the Design_Studio 730 via the model inputinterface shown as the connector 732. The Composer Agent 720 (e.g., anagent configured to perform the composer role) may analyze theapplication model and construct an equivalent model as a lattice ofinterconnected micro-capabilities, using the descriptors and ontologiesprovided by the MicroCapability metadata repository 550 via theconnector 721. An illustrative embodiment of this process is detailed inthe next section. A result of this processing by the Composer Agent 720may be a new model that includes a deployment and configuration plan.This model may be communicated to the plurality of Deployer Agents 710(e.g., agents configured to perform the deployer role) which mayconstruct the embodiment of the application using the appropriatecombination of compatible embodiments of micro-capabilities as providedin the plurality of MicroCapability implementation repositories 620 andas directed by the deployment model communicated through connection 722from the Composer Agent 720 to the plurality of Deployer Agents 710. TheDeployer Agents 710 may be responsible to load and activate theapplication in the appropriate Computing_Resources 110 through theirconnection 712.

Application Construction Process

FIG. 8 outlines an illustrative micro-capability selection and chainingprocess according to at least one embodiment. The process is entered at800, retrieving the Behavior Specification from the application model inaction 801. Action 802 derives a detailed Function Graph from thealready function-oriented behavior model acquired in the previous action801. The root of the Function Graph also may hold the Start Conditionsof the application, a complex structure composed from invocationparameters, preconditions and the function's algorithm classification.In action 803, these Start Conditions are set as the seed conditions fora micro-capability search. This search may be performed as an iterativeexecution of the MicroCapability Selection Pattern detailed as 810. TheStart Conditions may be matched against the ontology of MicroCapabilityDescriptors 510 and related API_Descriptors 511 in the MicroCapabilitymetadata repository 550. This search is performed in action 811.Multiple matches are supported, which may have different quality offulfillment of the Start Conditions but are all ontologically valid, forwhich the search algorithm intentionally produces a plurality ofresults. All results derived in action 811 can be evaluated and aqualitative metric of fulfillment (Behavior Metric) may be calculated inaction 812. This search is repeated until the set of candidatemicro-capabilities is determined to be exhausted in action 813. Based onthe current Start Conditions, the best fitting micro-capability may beselected in action 814 and returned into the main process together withits associated Behavior Metric through the exit from the MicroCapabilitySelection Pattern sub-process at action 815.

The returned Behavior Metric may be evaluated against the functionrequirements in action 805 and the returned micro-capability can beeither accepted or rejected. If it is rejected, Selection Criteria andStart Conditions may be altered and the search pattern restarted. If thereturned micro-capability is accepted, then it can be chained topreceding micro-capabilities in action 806 and the Start Conditionsupdated. This iterative process is repeated until the whole FunctionGraph is satisfied. This decision is made in action 807. In action 808,the final Start conditions may be converted into Terminal Conditions,which reflect the final function results and associated post-conditions.The completed MicroCapability Composition Plan can be published forsubsequent deployment through the Deployer Agents (e.g., agentsconfigured to perform the deployer role).

Application Deployment Process

FIG. 9 shows an illustrative process of deployment and configuration ofmicro-capability-based applications according to at least oneembodiment. After entering the process at 900, the ApplicationDeployment Model may be retrieved by the Deployer Agent 710 (e.g., agentconfigured to perform the deployer role) in action 901. This model mayinclude the MicroCapability Composition Plan and the model of thechained micro-capabilities as produced by the Composer Agent 720 (e.g.,agent configured to perform the composer role) and described in previoussection. The Deployer Agent 710 may require additional information aboutthe set of Computing_Resource embodiments intended as deploymenttargets, which can be supplied in action 902. This leading DeploymentAgent may contact the Deployment Agents associated with theComputing_Resource embodiments to retrieve the descriptors for eachtarget Computing_Resource embodiments in action 903. In action 904, theleading Deployment Agent may calculate the actual Deployment Plan andcommunicate it to all involved target Deployment Agents. Each of thetarget Deployment Agents may evaluate independently if all requiredmicro-capability embodiments (in a form compatible with theComputing_Resource embodiment) are available at the locus of theComputing_Resource embodiment 905. If this is not the case, thenembodiments of all required micro-capabilities may be acquired by thetarget Deployment Agent through the communication facilities from theclosest compatible micro-capability implementation repository 620 inaction 906. This action may not be performed if all required embodimentsof micro-capabilities are already present at the locus of the targetComputing_Resource embodiment. Finally at action 907, the Deployer Agentassociated with the target Computing_Resource embodiment performs theaggregation of micro-capability embodiments guided by the DeploymentPlan and as explained herein.

This results in an executable application deployed into the targetComputing_Resource embodiment at the exit action 908 of the deploymentprocess.

Computing Environment

FIG. 10 shows an illustrative computing environment 1010 for managing(e.g., developing, executing, and/or the like) a functional model-basedapplication 1036 according to some embodiments. As described herein, auser 1012 (e.g., an individual, group of individuals, computing device,group of computing devices, and/or the like), may utilize a computersystem 1020 (e.g., a node) of the environment 1010 to develop,configure, deploy, and/or the like, a functional model-based application1036. To this extent, the computer system 1020 may provide a functionalmodeling interface 1030, which can enable the user 1012 to define,modify, and/or the like, one or more aspects of the functionalmodel-based application 1036 using any solution. Additionally, thecomputer system 1020 may include a distributed operating system 1032,which may enable execution of the functional model-based application1036 on the computer system 1020.

The computer system 1020 is shown including a processing component 1022(e.g., one or more processors), a storage component 1024 (e.g., astorage hierarchy), an input/output (I/O) component 1026 (e.g., one ormore I/O interfaces and/or devices), and a communications pathway 1028.In general, the processing component 1022 executes program code, such asa functional modeling interface 1030, which is at least partially fixedin the storage component 1024. While executing program code, theprocessing component 1022 may process data, which can result in readingand/or writing transformed data from/to the storage component 1024and/or the I/O component 1026 for further processing. The pathway 1028provides a communications link between each of the components in thecomputer system 1020. The I/O component 1026 may comprise one or morehuman I/O devices, which enable a human user 1012 to interact with thecomputer system 1020 and/or one or more communications devices to enablea system user 1012 to communicate with the computer system 1020 usingany type of communications link. To this extent, the computer system1020 may manage a set of interfaces (e.g., graphical user interface(s),application program interface, and/or the like) that enable human and/orsystem users 1012 to interact with the software executing thereon, suchas the functional modeling interface 1030. Furthermore, the functionalmodeling interface 1030 may cause the computer system 1020 to manage(e.g., store, retrieve, create, manipulate, organize, present, or thelike) the data, such as one or more functional model-based applications1036, using any data management solution.

In any event, the computer system 1020 may comprise one or more generalpurpose computing articles of manufacture (e.g., computing devices)capable of executing program code installed thereon. As used herein, itis understood that “program code” means any collection of instructions,in any language, code or notation, that cause a computing device havingan information processing capability to perform a particular functioneither directly or after any combination of the following: (a)conversion to another language, code or notation; (b) reproduction in adifferent material form; and/or (c) decompression.

As used herein, the term “component” means any configuration ofhardware, with or without software, which implements the functionalitydescribed in conjunction therewith using any solution. The term “module”means program code that enables a computer system 1020 to implement thefunctionality described in conjunction therewith using any solution.When fixed in a storage component 1024 of a computer system 1020 thatincludes a processing component 1022, a module is a substantial portionof a component that implements the functionality. Regardless, it isunderstood that two or more components, modules, and/or systems mayshare some/all of their respective hardware and/or software.Furthermore, it is understood that some of the functionality discussedherein may not be implemented or additional functionality may beincluded as part of the computer system 1020.

When the computer system 1020 comprises multiple computing devices, eachcomputing device may have only a portion of a software application, suchas the functional modeling interface 1030 and/or the functionalmodel-based application 1036, fixed thereon (e.g., one or more functionmodels 1038 of the application 1036). However, it is understood that thecomputer system 1020 and program code (e.g., a software application) areonly representative of various possible equivalent computer systems thatmay perform a process described herein. To this extent, in otherembodiments, the functionality provided by the computer system 1020 andprogram code executing thereon can be at least partially implemented byone or more computing devices that include any combination of generaland/or specific purpose hardware with or without program code. In eachembodiment, the hardware and program code, if included, can be createdusing standard engineering and programming techniques, respectively.

Regardless, when the computer system 1020 includes multiple computingdevices, the computing devices may communicate over any type ofcommunications link. Furthermore, while performing a process describedherein, the computer system 1020 may communicate with one or more othercomputer systems using any type of communications link. In either case,the communications link may comprise any combination of various types ofwired and/or wireless links; comprise any combination of one or moretypes of networks; and/or utilize any combination of various types oftransmission techniques and protocols. In an embodiment, the computersystem 1020 comprises an application server, which communicates withusers 1012 over the Internet.

As discussed herein, the functional model-based application 1036 may berepresented by a set of function models 1038. Each function model 1038may define a function as a set of hardware-independent actions. Afunction model 1038 may reference one or more micro-functions 1034,which can be made available for use in defining the functionalmodel-based application 1036 in a distributed operating system 1032. Tothis extent, the functional modeling interface 1030 may provide aninterface that enables a user 1012 to define, modify, and/or the like, afunction model 1038 by selecting one or more micro-functions 1034 anddefining the corresponding conditions, criteria, chain, and/or the like,as described herein.

When desired, the user 1012 may deploy the functional model-basedapplication 1036 for execution in a target computing environment 1010.As part of the deployment process, the functional model-basedapplication 1036 may be converted into an executable application by aset of nodes, such as the computer system 1020, in the target computingenvironment having a distributed operating system 1032. In particular,the set of nodes in the target computing environment can comprise adistributed operating system 1032, which includes one or more agents1033, which are configured to process the function model(s) 1038 of theapplication 1036. Such processing can utilize the micro-functions 1034to convert device-independent references for the correspondingmicro-functions into implementations of the micro-functions capable ofperforming the corresponding micro-function within the target computingenvironment as contextualized micro-capabilities 1010. Themicro-functions 1034 may provide the functional model-based application1036 with unified access and usage of resources independent of theirembodiment. As discussed herein, such a conversion may occur inreal-time during execution of the functional model-based application1036, thereby enabling the execution of the functional model-basedapplication 1036 to be highly responsive to changes to the targetcomputing environment.

The computing environment 1010 may include a fabric of interconnectedcomputing resources, which includes computer system 1020 as well as oneor more additional computer systems 1021A, 1021B. Each computer system1021A, 1021B can be configured similar to the computer system 1020,although a computer system 1021A, 1021B may not include a functionalmodeling interface 1030 and/or a distributed operating system 1032. Asillustrated, each of the computer systems 1020, 1021A, 1021B maycommunicate with each other using any solution. When a functionalmodel-based application 1036 is deployed to the computing environment1010, the computer system 1020 may utilize one or more resources ofanother computer system 1021A, 1021B to execute the functionalmodel-based application 1036. Such utilization can include providing atleast a portion of the functional model-based application 1036 forexecution on the computer system 1021A, 1021B (e.g., when the computersystem 1021A, 1021B includes a distributed operating system 1032).

As described herein, some embodiments may address significant issueswith respect to the design, development, and deployment of softwareapplications. In particular, some embodiments can allow a user to designand deploy an application using a functional modeling solution, suchthat the application is not restricted to execution in any particulartarget computing environment. Additionally, some embodiments can addresssignificant issued regarding the execution of applications indistributed computing environments. In particular, as the amount and/ortypes of resources available in these computing environments can befrequently changed, binding of the software application to a particularconfiguration of computing resources can be delayed until such bindingis required during execution of the application.

Various Embodiments

While shown and described herein as a method and system for managing(e.g., designing, constructing, deploying, using, and modifying)software applications on a plurality of computing resources, which canoperate collaboratively and in parallel, it is understood that variousalternative or additional embodiments may be provided. For example, invarious embodiments, a computer program is fixed in at least onecomputer-readable medium, which when executed, enables a computer systemto manage software applications on a plurality of computing resources.To this extent, the computer-readable medium includes program code,which enables a computer system to implement some or all of a processdescribed herein. It is understood that the term “computer-readablemedium” comprises one or more of any type of tangible medium ofexpression, now known or later developed, from which a copy of theprogram code can be perceived, reproduced, or otherwise communicated bya computing device. For example, the computer-readable medium cancomprise: one or more portable storage articles of manufacture; one ormore memory/storage components of a computing device; paper; and/or thelike.

In another embodiment, a method provides a copy of program code, whichenables a computer system to implement some or all of a processdescribed herein. In this case, a computer system may process a copy ofthe program code to generate and transmit, for reception at a second,distinct location, a set of data signals that has one or more of itscharacteristics set and/or changed in such a manner as to encode a copyof the program code in the set of data signals. Similarly, someembodiments provide a method of acquiring a copy of the program code,which includes a computer system receiving the set of data signalsdescribed herein, and translating the set of data signals into a copy ofthe computer program fixed in at least one computer-readable medium. Ineither case, the set of data signals can be transmitted/received usingany type of communications link.

In still another example, a method provides generating a system formanaging software applications on a plurality of computing resources. Inthis case, the generating may include configuring a computer system,such as the computer system 1020 (FIG. 10), to implement a method ofmanaging software applications on a plurality of computing resources.The configuring may include obtaining (e.g., creating, maintaining,purchasing, modifying, using, making available, or the like) one or morehardware components, with or without one or more software modules, andsetting up the components and/or modules to implement a processdescribed herein. To this extent, the configuring can include deployingone or more components to the computer system, which can comprise one ormore of: (1) installing program code on a computing device; (2) addingone or more computing and/or I/O devices to the computer system; (3)incorporating and/or modifying the computer system to enable it toperform a process described herein; and/or the like.

The present invention(s) are described above with reference to exampleembodiments. It will be appreciated that various modifications may bemade and other embodiments may be used without departing from thebroader scope of the present invention(s). Therefore, these and othervariations upon the example embodiments are intended to be covered bythe present invention(s).

What is claimed is:
 1. A system that dynamically determines desiredfunctionality based on context of real-time events, constructsfunctional components based on the desired functionality, and executesthe functional components in real-time on a deployment target as acontextually-motivated service, the system comprising: one or moreprocessors; and memory storing instructions that, when executed by theone or more processors, cause the system to perform: detecting one ormore real-time events and determining a context based on the one or morereal-time events; fetching an application model based on the context andmeta-data associated with the one or more real-time events, theapplication model referencing one or more micro-functions, eachmicro-function being a declarative model of one or more atomic functionsand including at least one pre-condition descriptor and at least onepost-condition descriptor; constructing a functional graph based on theone or more micro-functions of the application model; transforming theone or more micro-functions into a plurality of micro-capabilities, eachmicro-capability of the plurality of micro-capabilities being capable ofsatisfying at least one pre-condition of the at least one pre-conditiondescriptor and at least one post-condition of the at least onepost-condition descriptor by: determining at least one computingresource for execution of at least one of the plurality ofmicro-capabilities by matching pre-conditions of the at least onemicro-capability of the plurality of micro-capabilities and matchingpost-conditions of the at least one micro-capability of the plurality ofmicro-capabilities; enable execution and configuration of the at leastone micro-capability on the at least one computing resource by providingaccess in a target environment to an application program interface(API), the API capable of calling the at least one micro-capability toconfigure the at least one micro-capability on the at least onecomputing resource and execute the micro-capability on the at least onecomputing resource; receiving a request to execute and configure the atleast one micro-capability on the at least one computing resource fromthe target environment; executing and configuring the at least onemicro-capability on the at least one computing resource; and providingan output of the at least one micro-capability from the at least onecomputing resource to the target environment.